Quantum Noise Removal in X-Ray Images with Adaptive Total Variation Regularization

Prasath, V.B. Surya

doi:10.15388/Informatica.2017.141

Informatica

Quantum Noise Removal in X-Ray Images with Adaptive Total Variation Regularization

Volume 28, Issue 3 (2017), pp. 505–515

V.B. Surya Prasath

https://doi.org/10.15388/Informatica.2017.141

Pub. online: 1 January 2017 Type: Research Article

Open Access

Received
1 November 2016

Accepted
1 April 2017

Published
1 January 2017

Abstract

Medical X-ray images are prevalent and are the least expensive diagnostic imaging method available widely. The handling of film processing and digitization introduces noise in X-ray images and suppressing such noise is an important step in medical image analysis. In this work, we use an adaptive total variation regularization method for removing quantum noise from X-ray images. By utilizing an edge indicator measure along with the well-known edge preserving total variation regularization, we obtain noise removal without losing salient features. Experimental results on different X-ray images indicate the promise of our approach. Synthetic examples are given to compare the performance of our scheme with traditional total variation and anisotropic diffusion methods from the literature. Overall, our proposed approach obtains better results in terms of visual appearance as well as with respect to different error metrics and structural similarity.

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Biographies

Prasath V.B. Surya

prasaths@missouri.edu

V.B.S. Prasath is currently an assistant research professor in the Department of Computer Science, University of Missouri-Columbia, USA. He received his PhD in mathematics from the Indian Institute of Technology Madras (IITM), India in 2009. He has been a postdoctoral fellow at the Department of Mathematics, University of Coimbra, Portugal, for two years. From 2012 to 2015 he was a postdoctoral fellow with the Computational Imaging and VisAnalysis (CIVA) Lab at the University of Missouri-Columbia working on various image processing and computer vision problems. He had summer internships/visits at Kitware Inc., USA, The Fields Institute, Canada, and Institute for Pure and Applied Mathematics (IPAM), University of California Los Angeles (UCLA), USA. His main research interests include regularization methods for inverse and ill-posed problems, optimization, variational, PDE based image processing, and computer vision with applications in remote sensing, bio-medical imaging, and biometrics domains.

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Keywords

total variation adaptive regularization quantum noise X-ray images denoising

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